The World Through a Bat's Ears

Introduction

People traditionally associate bats with vampires, Dracula, and other unpleasant matters. But watching them fly around in the dark of the Negev Desert in Israel as a young child, I became fascinated by the magical movements of these mysterious creatures. Wherever I visited, wherever I lived, bats were there.

The mouth of a cave

A year ago a scientist I knew told me about some raw material he had from a field expedition to Zimbabwe's Mana Pools National Park that had never been analyzed. So I filled my bedroom with leftover bat food—the wings and legs of katydids, ant lions, moths, and cicadas—and wrote a paper about the results for African Zoology. I wanted, however, to go further—into the field to collect my own data. This past summer I found a course offered by an Ontario university that would allow me to conduct my own research with sophisticated equipment. I leapt at the opportunity.

My interests have not exactly been understood by my friends. When I told a classmate I was going to study the behavioral ecology of bats, she exclaimed, "Eww! Bats are creepy!" But as I began to learn more about bats, I came to appreciate these small, often misunderstood animals.

A bat is a flying mammal.

Bats are the only flying mammals. Their bone structure is like that of humans, and yet they have a special ability to move about and locate their prey in the night skies. Lazzaro Spalanzani said as early as 1794 that bats could "see" with their ears. In 1940 Donald Griffin confirmed Spalanzini's reports and called the phenomenon "echolocation." By echolocation bats produce sounds and use their echoes to determine the location of objects, including prey. According to Griffin, by comparing the original signal with returning echoes, bats use sonar calls to get information about their target, its distance, direction, and nature.

Bats that feed in flight are called aerial-hawkers or aerial-feeding bats. They use echolocation to find, follow, and gather information about their airborne prey. When echolocating bats approach a target, they move in three phases. The first is a "search phase," which is a call with constant features and long intervals between the calls. The second is an "approach phase," and the last phase is called a "feeding buzz." During a feeding buzz the bat greatly shortens the duration of its calls and the intervals between them.

The echolocation calls of bats have two major patterns. The first pattern is called a high duty cycle, in which bats separate the pulse and the echo in frequency. These calls are long, separated by short periods of silence, and dominated by a single frequency. The second pattern is called a low duty cycle, in which bats separate the pulse and the echo in time. These calls are short, separated by long periods of silence, and are not dominated by a single frequency (Fenton and Ratcliffe, p. 612).

Bats can vary features of their echolocation calls. For example, they can change the duration, the frequency, and the cadence or the intervals between calls (inter-pulse intervals).

The features of a bat's call, or its design, affect what a bat hears. The wavelengths of sounds differ between frequencies, lower frequencies having longer wavelengths. Wavelengths affect the level of detail a bat can acquire about its target. Bats can get more detail by using higher frequencies with shorter wavelengths.

The frequencies of the echolocation calls of aerial-hawking bats may be influenced by various factors, such as the body size of the bat, the defenses used by their prey, environmental factors, and atmospheric attenuation (the reduction in the level of sound energy over distance) of bat signals. Biologists have also found that bats change their echolocation calls according to habitat and as a result of social setting.

An echolocating bat receives echoes from more than its flying prey. Echoes are also produced from objects such as trees and walls. These are called "clutter." Clutter can cover up or mask echoes that are coming from prey.

M. lucifugus hanging in a tree

The ability of bats to vary features of their echolocation calls suggests that they will adjust their calls to allow them to operate more successfully in cluttered (closed) environments. Although several researchers have spoken about clutter as perceived by bats, there have been few efforts to quantify either clutter or bats' responses to it. I wanted to know if and how echolocating bats respond to clutter. What can they do to better orientate in cluttered environments?

To find out, I arranged to record the echolocation calls of little brown bats, Myotis lucifugus, as they flew in four situations with different degrees of clutter. After recording, I analyzed selected calls, used statistical analyses to assess variation in the bats' calls, and considered the implications of my results.

M. lucifugus belongs to the Vespertilionidae family and is an aerial-hawking, insectivorous bat. Its echolocation calls are short, separated by long periods of silence, and are not dominated by a single frequency.

During my fieldwork, I had watched little brown bats fly adroitly in the open and in cluttered situations, so I predicted that these bats would adjust their calls according to the setting in which they operated. I hypothesized that in response to the degree of clutter, these bats would adjust the features of their calls, such as duration, frequency, and the intervals between calls, shortening the duration and the intervals between calls and increasing the frequency for shorter wavelengths in response to the need to avoid masking important echoes.

Materials and Methods

Sarah with recording equipment

I recorded the bats' echolocation calls with an Avisoft-Ultrasoundgate 116 microphone operated with a Dell Latitude D800 PC and Avisoft-USG software on 17 August 2004 at the Renfrew Mine in Ontario, Canada.

Entrance to abandoned mine

In each setting, the microphone was mounted on a tripod, pointed at 60 degrees up from the horizontal. I recorded the bats' calls in four settings with different degrees of clutter: (a) over an open field, where the nearest clutter (trees) was 6.3 meters away; (b) in vegetation, where the nearest branches and leaves were 0.7 meters away; (c) at the entrance to an abandoned mine to record approaching bats; and (d) within the mine adit (the nearly horizontal passage from the surface into the mine). In (c), I recorded the calls of bats as they approached the entrance to the mine, which was 1.7 m wide by 2.6 m high, while in (d) I recorded bats within the mine adit, which was 1.8 m wide by 2.8 m high, and 1.5 meters from a gate composed of a series of 2 cm bars every 16-17 cm, an obstacle they had to navigate in order to move deeper into the mine.

In the laboratory, I reviewed my hundreds of recordings and selected suitable calls for analysis. Calls selected were those where the signal was at least 10% stronger than the background.

Chart 1: Bat call waveforms - An example of a strong call I chose to analyze is marked with an asterisk, while weak calls I chose not to use are indicated with arrows. I chose calls where the amplitude was above 10%.

I then analyzed the calls using BatSoundPro software. For each of the four settings, I analyzed three sequentially produced echolocation calls taken from three call sequences. Call sequences were at least 60 seconds apart. I measured call duration (Dur) and inter-pulse interval (IPI) from the time amplitude and spectrogram displays (Chart 2).

Chart 2: Examples of calls recorded in each of the four settings: open, forest, entrance of the mine, and in the mine adit. Measurements were made of call duration and inter-pulse interval from the time amplitude and spectrogram displays. In the time-amplitude graph, the horizontal axis is measured in milliseconds (ms), and the vertical axis is measured in percent (%). In the spectrogram display, the horizontal axis is measured in milliseconds (ms), and the vertical axis is measured in kilohertz (kHz). Echoes and harmonics are also displayed in the FFT display.

Power spectra were used to produce three measures of sound frequency (in kHz): lowest frequency (LF), frequency of maximum energy (FME), and highest frequency (HF). Frequency of maximum energy (most intense frequency or peak frequency) is the frequency that exhibits the greatest relative power on the power spectrum. I measured the frequency of maximum energy from the fast Fourier transform (FFT) power spectrum, the highest and the lowest frequency from the FFT at -10 dB from FME (Chart 3). These data were recorded in a Microsoft Excel data sheet which I designed.

Chart 3: An example of a call in the fast Fourier transform (FFT) display showing how lowest frequency (LF), highest frequency (HF), and frequency of maximum energy (FME) were measured. The horizontal axis is measured in kilohertz (kHz), and the vertical axis is measured in decibels (dB).

I then statistically analyzed my data using statistical software SPSS 12.0, following Biscardi et al (in press). I transferred the call data from Microsoft Excel to SPSS. I then used a Multiple Analysis of Variance (MANOVA) to assess the level of variation in the call features I had measured (Dur, FME, HF, LF and IPI) according to their settings. Since there was significant variation, I proceeded to use Discriminate Function Analyses (DFA) to assess the accuracy with which bat calls could be classified into the different clutter settings. I then proceeded with a DFA to determine the parameters that were most important in identifying the calls, as well as the accuracy with which the calls could be classified as coming from a particular setting. Lastly, I examined the cross-validated classification results. (See Appendix 1: Note About Statistics.)

Results

I analyzed a total of 36 echolocation calls of M. lucifugus, nine for each of the four settings. The descriptive statistics are presented in Table 1. Calls produced in the open setting are longer in duration, and are produced at longer inter-pulse intervals and at lower frequencies, than calls produced in the three closed (cluttered) settings. Among the cluttered settings, calls produced in the forest vegetation are longer in duration, and are produced at longer inter-pulse intervals and at lower frequencies, than calls produced in or near the more restricted environment of the mine. The results from the multiple analysis of variance (MANOVA) (Table 2) revealed that the variation in call features according to setting was significant. This level of variation was validated using discriminate function analyses (DFA) to classify the data according to setting.

Table 1. The descriptive statistics (mean standard deviation) of the echolocation calls I recorded and analyzed. Presented here are sample size (N), duration (Dur), frequency of maximum energy (FME), highest frequency (HF), lowest frequency (LF), and inter-pulse interval (IPI). The recordings were made in an open field, in vegetation, at the entrance of the mine, and within the mine adit.

Setting

N

Dur(ms)

FME(kHz)

HF (kHz)

LF (kHz)

IPI(ms)

Open Field

9

8.32.1

35.48.9

44.09.2

32.86.8

152.078.4

Vegetation

9

3.00.5

47.63.4

51.53.8

44.03.3

70.913.0

Entrance of Mine

9

2.10.6

53.36.2

62.37.2

45.34.3

38.019.7

Inside Mine

9

2.30.3

52.32.0

64.512.2

47.34.0

61.219.7

Table 2. The results of MANOVA analyses of the data from the echolocation calls reveal significant variation in call features according to the setting (degree of clutter) in which the calls were recorded. Here, "all" means all the call features measured, Dur, FME, HF, LF, and IPI. For each F value, the first subscript number is the hypothesis degrees of freedom, and the second is the error degree of freedom.

CallFeatures

Wilk'sLambda

F-value

Probability

Setting

All

0.003

F5, 16 =980

< 0.001

Setting

Dur, FME

0.004

F2, 31 =4074

< 0.001

Having determined there was significant variation, I used a series of DFA analyses to explore changes in bat echolocation calls according to clutter (Tables 3a-3d). The first analysis (Table 3a) using all call features showed 75% correct classification of bat calls by clutter setting. The DFA analysis revealed that Function 1 (of 3) accounted for 97.7% of the variation, and Function 1 largely reflected the contributions of two call features, duration and frequency of maximum energy. Dur and FME, therefore, were the two most important parameters distinguishing the four settings. I then used only duration and FME for another DFA (Table 3b), and the level of correct classification fell to 55.6%. This indicates that other call features besides Dur and FME add to the precision of the DFA classification, and supports the view that bats are controlling all the features or the whole signal. It is interesting to note that although the DFA analysis sometimes failed to classify calls correctly among the different closed settings, a closed call was never classified as an open call, and an open call was only rarely classified as a closed call. Where an open call was classified as a closed call, it was always classified as a call in the vegetation setting and never as a call at the entrance to or within the more restricted environment of the mine. Amongst the closed calls, the level of correct classification was higher—83.3% in Table 3a and 77.8% in Table 3b—in the vegetation setting than at the entrance or within the mine.

Table 3a. The results of a cross-validated DFA showing the percent of calls identified correctly to setting using the call data I collected. Here, setting 1 is the open situation, setting 2 is vegetation, setting 3 is the mine entrance, and setting 4 is inside the mine. The percentage on the diagonal is the percentage of correct classifications. Overall, the cross-validated DFA correctly assigned calls by setting 75% of the time.

Setting

1

2

3

4

Total

1

83.3

16.7

0

0

100

2

0

83.3

0

16.7

100

3

0

16.7

66.7

16.7

100

4

0

16.7

16.7

66.7

100

Table 3b. The results of a cross-validated DFA showing the percent of calls identified correctly to setting using data only on Dur and FME. Here, setting 1 is the open situation, setting 2 is vegetation, setting 3 is the mine entrance, and setting 4 is inside the mine. The percentage on the diagonal is the percentage of correct classifications. Overall, the cross-validated DFA correctly assigned calls by setting 55.6% of the time.

Setting

1

2

3

4

Total

1

89.9

11.1

0

0

100

2

0

77.8

0

22.2

100

3

0

55.6

11.1

33.3

100

4

0

11.1

44.4

44.4

100

In the next DFA (Table 3c), I used all call features and compared open versus closed (cluttered) situations. The DFA correctly associated calls with setting 95.8% of the time. In this analysis, when I just used Dur and FME (Table 3d), calls were correctly identified 97.2% of the time. It is interesting that in both of these analyses (Table 3c and 3d), the cluttered calls were identified 100% of the time, but the calls produced in the open were not. In examining any echolocation call, I do not know exactly where the bat was located at the time of call production. Although my experimental setup was designed to minimize the chances of a bat in the open hearing clutter from vegetation and fixed objects, other bats may also be important sources of clutter.

Table 3c. The results of a cross-validated DFA demonstrating the percent of calls correctly identified as either open or closed using all the call features. Settings 2, 3, and 4 are classified as closed (2). The open situation is (1). The percentage on the diagonal is the percentage of correct classifications. The DFA correctly assigned calls 95.8% of the time.

Degree of Clutter

1

2

Total

1 - open

83.3

16.7

100

2 - closed

0

100

100

Table 3d. The results of a cross-validated DFA showing the percent of calls correctly identified as either open or closed using only Dur and FME. Settings 2, 3, and 4 are classified as closed (2). The open situation is (1). The percentage on the diagonal is the percentage of correct classifications. The DFA overall correctly assigned calls 97.2% of the time.

Degree of Clutter

1

2

Total

1 - open

88.9

11.1

100

2- closed

0

100

100

My results demonstrate that clutter influences the structure of bat echolocation calls. Free-flying M. lucifugus adjusted their calls according to clutter. The results confirm my hypothesis that bats vary the features of their echolocation calls according to the settings in which they are operating. My findings are based on my identification of different forms and degrees of clutter (from an open field to a forest to a mine adit) and my analyses of bats' responses to it, and support earlier suggestions of biologists such as Kalko (1995) and Neuweiler et al (1987), who have proposed a connection between echolocation call design and clutter.

While Siemers and Schnitzler (2004) describe how different species of European Myotis adjusted their echolocation calls when hunting prey in clutter, and suggest that high-frequency signals are a good adaptive strategy for successfully capturing prey in clutter, their bats were hunting in captivity. Mine were in the wild, and were not hunting but rather approaching or entering a mating site (located in the mine).

My findings also indicate that increases in clutter for bats operating in the wild (from an open field to a forest to a mine adit) are met by increasing adjustments in bats' echolocation calls. In cluttered settings, in which there are more rapidly returning echoes, the bats shortened the duration of their calls and the inter-pulse interval. M. lucifugus appear to be using shorter calls, as well as higher frequencies, to obtain more information about close objects in cluttered settings.

Quantifying bats' responses to varying measures of clutter in the wild presents unique challenges. More detailed conclusions would benefit from analyses of the complexity of vegetation contours or foliage echoes and their impact on bat echolocation. Challenges in the wild also include the need to take adequate account of other bats as sources of clutter. Future research would benefit as well from an increase in the sample size (number of calls and number of sequences).

It is clear that bats adjust the design of their echolocation calls according to their situation. Situation-specific calls alert other bats to those conditions in which the bat is operating. Other bats within earshot of the Renfrew site could have used the calls I recorded to locate the entrance to the mine. This helps biologists to understand how bats can find important sites such as mating and hibernation sites. In other settings, biologists have demonstrated that free-flying bats in the wild listen to one another and keep track of what is happening to other bats within earshot.

In the future, the effects of clutter on the echolocation calls of other bats in other places could be examined. Larger, narrow-winged, fast-flying bats, such as some free-tailed bats, would be expected to be more wary of clutter and to adjust to it with greater difficulty. Recent suggestions that time of night could influence bat echolocation calls are unexplained and intriguing (Biscardi et al, in press). I would like to experiment with other circumstances, such as the darkness of the setting, which might have an impact on bats' echolocation calls.

How bats cope with clutter can have implications for the world beyond bats. Emulating bats, some people have designed sonar aids for the blind. The sonar aid has been successful in allowing users to navigate environments that humans would call cluttered. Kay's Auditory Spatial Perception Aid (KASPA), for example, is a sonic guide in the form of a device worn as a headband, or as miniature earpieces connected to a small unit attached to a cane. Ultrasonic waves are sent in the form of a high-frequency, narrow band of sound, and the echoes are received and transformed into electrical signals that are then processed to produce sounds audible to humans. Just as my results indicate that bats cope with clutter by producing shorter, more frequent calls of higher frequency, this sonar aid technology has controls that change the pulsating tone to higher frequencies of shorter duration for short-range use. Humans can use the same techniques as bats to "see" with their ears. Learning more about how bats handle clutter can provide valuable lessons for further development of sonar aids. Improvements to sonar aids which give the user greater control over the outgoing signal, and refine additional dimensions of the signal, may adjust the information that comes back. An answer for the next time someone calls bats "creepy"!

References

Books

Allen, Glover M. Bats. Harvard University Press, Cambridge, 1939.

Griffin, Donald R. Listening in the Dark. New Haven: Yale University Press, 1958.

Journal Articles

Bayefsky-Anand, Sarah. In press. "Effect of location and season on the arthropod prey of Nycteris grandis (Chiroptera: nycteridae)." African Zoology.

Biscardi, Stefania, et al. In press. "Data, sample size and statistics affect the recognition of species of bats by their echolocation calls." Acta Chiropterologica.

Borenstein, Johann and Iwan Ulrich. "The Guide Cane—A Computerized Travel Aid for the Active Guidance of Blind Pedestrians." Proceedings of the ISEE International Conference on Robotics and Automation. (1997): 1283-1288.

Lawrence, Beatrice D. and James A. Simmons. "Measurements of atmospheric attenuation at ultrasonic frequencies and the significance for echolocation by bats." Journal of the Acoustical Society of America 71 (1982): 585-590.

Appendix 1: Notes about statistics

I used SPSS, a statistical program to obtain descriptive statistics (Table 1) for the call features I analyzed, including mean and standard deviation.

Using SPSS, I performed MANOVAs (multiple analysis of variance) to determine the level of variation in echolocation call features according to setting (Table 2). The MANOVA provided several measures of the situation, and here I have presented values for Wilk's Lambda, F statistics (with two indications of degrees of freedom - hypothesis and error) as well as the probability values for each analysis. Biologists typically take P < 0.05 as an indication of significance. My probability (P) values exceed this criterion.

When MANOVAs reveal the presence of significant variation across features and treatments, it is acceptable to proceed to a DFA (Discriminant Function Analysis) for further consideration of the data. This classification process uses the call features and situations to generate indications of which factors (in my case call features) are most associated with variation between calls produced in different settings. The procedure uses the data to assign each call to a situation (setting in terms of clutter in my study) and reports percentages of correct assignments. In cross validation each case is classified by the functions derived from all cases other than that case.

The statistical analyses allow researchers to understand the "significance" of their findings and observe how different factors can interact.

More About This Resource...

Teacher Tip

Supplement a study of biology with an activity drawn from this winning student essay.

Ask students: What is echolocation? What animals use it? How do you think they are affected by echoes off of stationary objects when trying to locate prey?

Send students to this online article, or print copies of the essay for them to read.

In a one-page essay, have them explain in their own words what Sarah learned about how clutter influences the use of echolocation by bats.